Venkat Kapil, PhD

I am a computational chemistry and material scientist working at the crossroads of machine learning, quantum mechanics, and statistical mechanics.

My day job is to develop rigorous, efficient, and scalable simulation techniques with the accuracy and complexity of experiments.

Some of the methods that my group works on include:

1. How can large-scale machine-learning models of interatomic potentials and electronic properties be trained?
   

2. What is the most data-efficient approach to training machine learning potentials directly to explicitly correlated electronic structure theory level?
   

3. How to develop classical algorithms to incorporate quantum nuclear effects?
   

4. How do we rigorously predict non-linear vibrational spectroscopy in molecular and condensed phase systems?

Some of the applications we like are:

1. What factors influence the relative stabilities of polymorphs of drug-like molecules, and can we predict them consistently with kJ/mol precision?
   

2. How do the phase behaviours and chemical reactivity change at interfaces and in nanometer-scale confinement, and how do these influence batteries and catalysts?
   

3. How do battery materials and catalysts behave at the molecular scale in operando conditions and can we predict their lifetime?